Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "227" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 22 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 22 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2459998 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 3.238023 | -0.166928 | 2.062188 | -1.206738 | 0.427341 | -0.691915 | 9.580685 | 4.848106 | 0.4782 | 0.5882 | 0.4047 | nan | nan |
| 2459997 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 3.414768 | -0.198283 | 2.107380 | -1.197504 | 0.781024 | -1.172762 | 14.302718 | 7.385592 | 0.4984 | 0.6020 | 0.4057 | nan | nan |
| 2459996 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 3.312428 | -0.515156 | 2.897425 | -0.901756 | 0.450582 | 15.083222 | 9.153849 | 4.037592 | 0.5173 | 0.6074 | 0.4145 | nan | nan |
| 2459995 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 3.792767 | 0.901960 | 2.327172 | -1.141891 | 0.603906 | 8.193369 | 9.385026 | 7.199739 | 0.5033 | 0.5978 | 0.3987 | nan | nan |
| 2459994 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 4.234744 | 0.153361 | 2.113484 | -1.340486 | 1.410052 | 0.614824 | 4.489014 | 2.952727 | 0.4867 | 0.5969 | 0.4046 | nan | nan |
| 2459993 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 5.328929 | 0.045282 | 1.707326 | -1.072760 | 2.806313 | 3.693880 | 5.561256 | 3.987592 | 0.4702 | 0.5990 | 0.4201 | nan | nan |
| 2459991 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 5.586228 | 0.113076 | 1.996496 | -0.878990 | 2.276265 | 7.059631 | 4.599889 | 1.463113 | 0.4778 | 0.5899 | 0.4160 | nan | nan |
| 2459990 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 4.515980 | -0.280891 | 1.956959 | -0.350652 | 2.182825 | -0.036282 | 4.305663 | 0.358085 | 0.4737 | 0.5961 | 0.4173 | nan | nan |
| 2459989 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 4.239114 | -0.216540 | 1.728642 | -0.344439 | 1.745685 | -0.869938 | 2.786007 | -0.187853 | 0.4725 | 0.5988 | 0.4193 | nan | nan |
| 2459988 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 5.192835 | -0.399299 | 1.949586 | -0.367930 | 2.769623 | -0.421225 | 3.513211 | 0.834268 | 0.4789 | 0.6003 | 0.4112 | nan | nan |
| 2459987 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 3.824976 | 0.437276 | 1.932860 | -1.381259 | 1.170799 | 7.145328 | 6.283015 | 1.750427 | 0.4891 | 0.5980 | 0.4022 | nan | nan |
| 2459986 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 5.033675 | -0.097493 | 2.124066 | -1.033932 | 1.681411 | 3.410598 | 3.611980 | -0.404385 | 0.5264 | 0.6329 | 0.3661 | nan | nan |
| 2459985 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 4.435008 | 0.106431 | 1.967420 | -1.091713 | 1.128845 | 6.479981 | 8.640402 | 2.998805 | 0.4968 | 0.6050 | 0.4129 | nan | nan |
| 2459984 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 3.900299 | 0.635615 | 2.041919 | -1.181360 | 1.840583 | 10.232791 | 4.033852 | 2.755186 | 0.5220 | 0.6159 | 0.3853 | nan | nan |
| 2459983 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 4.918176 | -0.341090 | 1.948820 | -0.693037 | 2.025578 | 6.186473 | 3.865350 | -0.405588 | 0.5095 | 0.6335 | 0.3721 | nan | nan |
| 2459982 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 3.907490 | -0.083531 | 1.887480 | -0.700511 | 0.517475 | -1.262104 | -0.315545 | -1.403241 | 0.6019 | 0.6851 | 0.3143 | nan | nan |
| 2459981 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 1.790008 | 0.453637 | -1.472221 | 0.347708 | -0.458462 | -0.355590 | 12.384212 | -0.405086 | 0.5690 | 0.6054 | 0.3687 | nan | nan |
| 2459980 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 1.972180 | 0.605609 | -1.504320 | -0.185623 | -0.284194 | -0.829623 | 1.124274 | -0.573789 | 0.6254 | 0.6518 | 0.2955 | nan | nan |
| 2459979 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 2.001682 | 0.291315 | -1.457719 | -0.111188 | -0.374903 | -0.942467 | 10.360066 | -0.368577 | 0.5612 | 0.6019 | 0.3706 | nan | nan |
| 2459978 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 1.888468 | 0.466872 | -1.465186 | 0.079468 | -0.217913 | -0.702086 | 12.078642 | -0.318509 | 0.5632 | 0.6025 | 0.3768 | nan | nan |
| 2459977 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 1.953582 | 0.590841 | -1.439265 | -0.159833 | 0.464393 | -0.484483 | 14.766487 | 3.224384 | 0.5265 | 0.5618 | 0.3382 | nan | nan |
| 2459976 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 1.909637 | 0.411898 | -1.541852 | -0.020315 | 0.408851 | -0.388740 | 11.825364 | 0.489314 | 0.5723 | 0.6092 | 0.3637 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 227 | N20 | RF_ok | ee Temporal Discontinuties | 9.580685 | 3.238023 | -0.166928 | 2.062188 | -1.206738 | 0.427341 | -0.691915 | 9.580685 | 4.848106 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 227 | N20 | RF_ok | ee Temporal Discontinuties | 14.302718 | 3.414768 | -0.198283 | 2.107380 | -1.197504 | 0.781024 | -1.172762 | 14.302718 | 7.385592 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 227 | N20 | RF_ok | nn Temporal Variability | 15.083222 | 3.312428 | -0.515156 | 2.897425 | -0.901756 | 0.450582 | 15.083222 | 9.153849 | 4.037592 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 227 | N20 | RF_ok | ee Temporal Discontinuties | 9.385026 | 3.792767 | 0.901960 | 2.327172 | -1.141891 | 0.603906 | 8.193369 | 9.385026 | 7.199739 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 227 | N20 | RF_ok | ee Temporal Discontinuties | 4.489014 | 4.234744 | 0.153361 | 2.113484 | -1.340486 | 1.410052 | 0.614824 | 4.489014 | 2.952727 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 227 | N20 | RF_ok | ee Temporal Discontinuties | 5.561256 | 5.328929 | 0.045282 | 1.707326 | -1.072760 | 2.806313 | 3.693880 | 5.561256 | 3.987592 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 227 | N20 | RF_ok | nn Temporal Variability | 7.059631 | 5.586228 | 0.113076 | 1.996496 | -0.878990 | 2.276265 | 7.059631 | 4.599889 | 1.463113 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 227 | N20 | RF_ok | ee Shape | 4.515980 | -0.280891 | 4.515980 | -0.350652 | 1.956959 | -0.036282 | 2.182825 | 0.358085 | 4.305663 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 227 | N20 | RF_ok | ee Shape | 4.239114 | -0.216540 | 4.239114 | -0.344439 | 1.728642 | -0.869938 | 1.745685 | -0.187853 | 2.786007 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 227 | N20 | RF_ok | ee Shape | 5.192835 | -0.399299 | 5.192835 | -0.367930 | 1.949586 | -0.421225 | 2.769623 | 0.834268 | 3.513211 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 227 | N20 | RF_ok | nn Temporal Variability | 7.145328 | 3.824976 | 0.437276 | 1.932860 | -1.381259 | 1.170799 | 7.145328 | 6.283015 | 1.750427 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 227 | N20 | RF_ok | ee Shape | 5.033675 | -0.097493 | 5.033675 | -1.033932 | 2.124066 | 3.410598 | 1.681411 | -0.404385 | 3.611980 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 227 | N20 | RF_ok | ee Temporal Discontinuties | 8.640402 | 0.106431 | 4.435008 | -1.091713 | 1.967420 | 6.479981 | 1.128845 | 2.998805 | 8.640402 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 227 | N20 | RF_ok | nn Temporal Variability | 10.232791 | 3.900299 | 0.635615 | 2.041919 | -1.181360 | 1.840583 | 10.232791 | 4.033852 | 2.755186 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 227 | N20 | RF_ok | nn Temporal Variability | 6.186473 | 4.918176 | -0.341090 | 1.948820 | -0.693037 | 2.025578 | 6.186473 | 3.865350 | -0.405588 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 227 | N20 | RF_ok | ee Shape | 3.907490 | 3.907490 | -0.083531 | 1.887480 | -0.700511 | 0.517475 | -1.262104 | -0.315545 | -1.403241 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 227 | N20 | RF_ok | ee Temporal Discontinuties | 12.384212 | 0.453637 | 1.790008 | 0.347708 | -1.472221 | -0.355590 | -0.458462 | -0.405086 | 12.384212 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 227 | N20 | RF_ok | ee Shape | 1.972180 | 0.605609 | 1.972180 | -0.185623 | -1.504320 | -0.829623 | -0.284194 | -0.573789 | 1.124274 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 227 | N20 | RF_ok | ee Temporal Discontinuties | 10.360066 | 2.001682 | 0.291315 | -1.457719 | -0.111188 | -0.374903 | -0.942467 | 10.360066 | -0.368577 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 227 | N20 | RF_ok | ee Temporal Discontinuties | 12.078642 | 0.466872 | 1.888468 | 0.079468 | -1.465186 | -0.702086 | -0.217913 | -0.318509 | 12.078642 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 227 | N20 | RF_ok | ee Temporal Discontinuties | 14.766487 | 1.953582 | 0.590841 | -1.439265 | -0.159833 | 0.464393 | -0.484483 | 14.766487 | 3.224384 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 227 | N20 | RF_ok | ee Temporal Discontinuties | 11.825364 | 0.411898 | 1.909637 | -0.020315 | -1.541852 | -0.388740 | 0.408851 | 0.489314 | 11.825364 |